STUDY ON ABILITY SHRINKAGE POROSITY FORMATION OF a380 ALUMINUM BY TAGUCHI METHOD NGHIÊN cứu về KHẢ NĂNG HÌNH THÀNH độ xốp CO NGÓT của hợp KIM NHÔM a380 BẰNG PHƯƠNG PHÁP TAGUCHI

STUDY ON ABILITY SHRINKAGE POROSITY FORMATION OF A380 ALUMINUM BY TAGUCHI METHOD.. 1Hung Yen University of Technology and Education 2Vocational Intermediate School No.14, Ninh Binh a air

STUDY ON ABILITY SHRINKAGE POROSITY FORMATION OF A380

ALUMINUM BY TAGUCHI METHOD

NGHIÊN C ỨU VỀ KHẢ NĂNG HÌNH THÀNH ĐỘ XỐP CO NGÓT CỦA HỢP KIM

NHÔM A380 B ẰNG PHƯƠNG PHÁP TAGUCHI

Anh Tuan Do 1,a , Tran Vung Vu 1b , Van Thuy Hoang 2c , Thi Mo Pham 2d

1Hung Yen University of Technology and Education

2Vocational Intermediate School No.14, Ninh Binh

a airgun631@gmail.com; b vutranvung@gmail.com;

c hoangthuy030@gmail.com; d sumosumo68@gmail.com

ABSTRACT

The current paper on shrinkage porosity formation of die casting for automobile part product, the following issues are focused: filling simulation, defect analysis, finally the use of the Taguchi multi quality analytical method to find the optimal parameters and factors to increase quality and efficiency with the aluminum A380 material die casting Experiments were conducted by varying molten alloy temperature, die temperature, plunger velocities in the first and second stage, and multiplied pressure in the third stage using L27 orthogonal array of Taguchi method After conducting a series of initial experiments in a controlled environment, significant factors for pressure die casting processes are selected to construct an appropriate multivariable linear regression analysis model for developing a robust performance for pressure die casting processes The appropriate multivariable linear model is

a useful and efficient method to find the optimal process conditions in pressure die casting associated with the minimum shrinkage porosity percent

Keywords: Taguchi method, die-casting, shrinkage porosity, aluminum A380,

optimization

Bài báo nói về quá trình hình thành xốp co ngót của vật đúc áp lực cho một phần sản

phẩm ô tô Các vấn đề sau đây được tập trung: mô phỏng điền đầy khuôn, phân tích các khuyết tật, cuối cùng là sử dụng phương pháp phân tích Taguchi đa chất lượng để tìm ra các thông số tối ưu và các yếu tố để tăng chất lượng và hiệu quả của hợp kim nhôm A380 đúc áp

lực cao Các thí nghiệm được tiến hành bằng cách thay đổi nhiệt độ nóng chảy hợp kim, nhiệt

độ khuôn đúc, vận tốc pittông trong giai đoạn đầu tiên và thứ hai, và áp lực giữ trong giai đoạn thứ ba sử dụng L27 mảng trực giao của phương pháp Taguchi Sau khi tiến hành một loạt các thí nghiệm ban đầu trong một môi trường kiểm soát, trong đó các yếu tố ảnh hưởng đến quá trình đúc áp lực được lựa chọn để xây dựng một mô hình phân tích hồi quy tuyến tính đa

biến phù hợp cho việc triển khai quá trình đúc áp lực cao Mô hình tuyến tính đa biến thích

hợp là một phương pháp hữu ích và là phương pháp hiệu quả cho quá trình tối ưu trong đúc

áp lực cao liên quan đến phần trăm tối thiểu của xốp co ngót

Từ khóa: phương pháp Taguchi, đúc áp lực, xốp co ngót, hợp kim nhôm A380, tối

ưu hóa

1 INTRODUCTION

High-pressure die casting (HPDC) process is significantly used in the industry for its high productivity and less post-machining requirement Due to light weight and good forming-ability, aluminum die casting plays an important role in the production of

transportation and vehicle components It has a much faster production rate in comparison to other methods and it is an economical and efficient method for producing components with low surface roughness and high dimensional accuracy All major aluminum automotive components can be processed with this technology The development of industrial die-casting and requirements for higher quality product, shorter development times and more complex geometry, the use of computer aided simulation has become essential to stay competitive

In 2001, M Avalle et al [1] compared the production of the original parts and standard specimens The study of die-casting defects and fatigue strength of aluminum also found that: defects in material fatigue strength are lower than the materials containing defects in castings

M Avalle made a total of three defects in a sample: the porosity, the cold fills and the aluminum oxide film They make the results: in particular, the tensile strength decreases linearly with the porosity range and other defects, the fatigue strength is not only related to defects with the nature of the material itself So that, improve the quality of the casting from reducing casting defects and materials selection

M Avalle in another study [2] for static and fatigue strength of a die cast aluminum alloy under different feeding conditions indicated that three batches of different samples conducted - neither the same gate nor flow channel design, would be porosity and impact casting defects, thereby affecting the static and fatigue strength

The HPDC castings production process has many defects, such as: shrinkage porosity, misrun, cold-shut, blister, scab, hot-tear… Several previous studies of defects in aluminum alloy by the method of HPDC and disability solutions Techniques such as cause-effect diagrams, design of experiments (DOE), casting simulation, if-then rules (Fuzzy Logic Controller), genetic algorithms (GA) and artificial neural networks (ANN) are used by various researchers for analysis of casting defects Dargusch et al [3] used pressure sensor in the cavity to make a confident statement of aluminum that molten metal velocity increases and porosity development with high pressure die- casting G.O Verran [4] used the design of experiments (DOE) to find out the best parameters in production and notice that: porosity low indices are related with low speeds from slow and fast shots and high upset pressures M Anijdan et al [5] used genetic algorithm (GA) methods to determine the optimum conditions leading to minimum porosity in aluminum alloy die casting V.D Tsoukalas [6,7] used the design of experiments (DOE) and genetic algorithm (GA) methods to determine the optimum conditions leading to minimum porosity in aluminum alloy die castings G.P Syrcos [8] used Taguchi method to determine the optimum conditions leading to casting density in aluminum alloy die castings

In this paper, the ProCAST® Software commercial is used for analysis casting defects and die filling simulation to enhance the quality and efficiency of die casting The Taguchi method control with design of experiments will be developed to improve aluminum die casting quality and productivity in the cold chamber die casting method After conducting a series of initial experiments in a controlled environment, significant factors for die casting processes are selected to find the optimal parameters to increase the aluminum die casting quality and efficiency

2 EXPERIMENTAL PROCEDURE

2.1 Die-casting body design

In order to understand how the casting generated defects start its source, casting design began to proceed from the casting wall thickness, holes, fillets, draft angles and to find out the ways designing better and faster Die casting of this study is provided through aluminum die-casting factory, so the die-casting body no changes The die-casting is designed on CATIA software, shown in Fig 1

Fig 1 Part product is designed

Moreover, the die casting material selection is very important; the nature of the material will directly affect the quality of the casting and die-casting parameters configuration, taking into account the casting and casting pressure, this study selects casting material as the aluminum alloy A380 The chemical composition of the aluminum alloy used in the experimental procedure is given in Table 1

Table 1 Chemical composition of the alloy A380 used

2.2 Taguchi design

Taguchi method is one of the efficient problems solving tools to upgrade the performance of products and processes with a significant reduction in cost and time involved [4, 6, 7, 8] Shrinkage porosity formation in pressure die casting is the result of a so much number of parameters Fig 2 shows a cause and effect diagram that was constructed to identify the casting process parameters that may affect die casting porosity In this case, holding furnace temperature, die temperature, plunger velocity in the first stage, plunger velocity in the second stage and multiplied pressure in the third stage were selected as the most critical in the experimental design [7, 8] The other parameters were kept constant in the entire experimentation The range of holding furnace temperature was selected as 640÷700°C, the range of die temperature as 180÷260° C, the range of plunger velocity in the first stage as 0.05÷0.35 m/s and in the second stage as 1.5÷3.5 m/s, the range of multiplied pressure in the third stage was chosen as 200–280 bars The selected casting process parameters, along with their ranges, are given in Table 2

Table 2 Parameters with their ranges and values at three levels

Process parameters Parameters range Level 1 Level 2 Level 3 Holding furnace temperature ( °C ) 640–700 640 670 700

Die temperatute ( °C ) 180–260 180 220 260

Plunger velocity, 1st stage (m/s) 0.05–0.35 0.05 0.2 0.35

Plunger velocity, 2nd stage (m/s) 1.5–3.5 1.5 2.5 3.5

Multiplied pressure (bars) 200–280 200 240 280

Fig 2 Cause and effect diagram

The experimental layout plan with five factors and three levels using L27 orthogonal array, 27 experiments were carried out to study the effect of simulation input parameters, shown in Table 3

Table 3 Experimental layout using an L 27 orthogonal array

Trials Holding Die Plunger Plunger Multiplied

furnace Temperature velocity velocity pressure temperature 1st stage 2nd stage

2.3 Die-casting process analysis

Analysis software is used as a ProCAST® commercial using finite element method analysis for a casting process In this study, all parameters can be able to affect the analysis process, choice of material aluminum A380 alloy die casting, cold chamber die casting method with H13 material molding The ProCAST with VIEWCAST module can provide temperature field, thermal cracking, flow field, solidification time, shrinkage analysis This paper focused on analysis of shrinkage porosity base on parameters input form Table 4, each experiment was repeated five times in order to reduce experimental errors

Table 4 Shrinkage porosity results of the L 27 array design

2.3.1 Analysis of casting defects

The analysis of defects simulated by ProCAST software with modules VIEWCAST can detect many types of disabilities casting Defective products do not necessarily reflect the loss

of the original function, for example, the internal pore trims acceptable The casting with the gating system and biscuit is show in Fig 3

Fig 3 Casting product

However, with large structural castings, defect analysis of this study focuses on maximum porosity in the selection casting, and the important parts of the casting shrinkage analysis (an important component), casting defect analysis are described as follows:

- Solid fraction may be available shrinkage prediction casting position, the present study

is in accordance with the theory prediction of defect, and ProCAST manual [9] referred to in the final period of solidification Shrinkage solid fraction prone is greater than 0.7, here as the reference value of 0.7 solid fractions When the solid fraction area below this value and the area around the solid phase rate rather than this value, we can predict this area shrinkage porosity occurred

- The maximum porosity analysis using the Shrinkage Porosity function ViewCAST

comes defined in the manual, in accordance with the ProCAST user manual Shrinkage [9], a volume fraction of 1% (0.01) or less shrinkage (naked eyes not visible micropores) and 1% (0.01) as compared to the above shrinkage porosity (visible to the naked eyes)

According to the above definition and with the solid fraction, it can be used to analyse basis of the maximum porosity

Shrinkage analysis:

Fig 4 Casting measurement area

For the amount of inspection shrinkage casting part used for the ViewCast module function for quantitative analysis, as shown in Fig 4

2.3.2 The analysis of variance (ANOVA)

The responding graph show in Fig 5 learned that the best combination for this study,

aluminum die casting shrinkage porosity defects: A 3 B 3 C 3 D 1 E 3

Fig 5 S/N Response graphs

2.3.3 Process parameter optimization using MVLR

The objective of the process optimization is to select the optimal control variables in aluminum die casting process in order to obtain the minimum porosity In this work, the fitness function used in the optimization procedure was based on the MVLR (Multivariable linear regression) model

In most case, the form of the relationship between the response and the independent variables is usually unknown Multiple linear regression (MLR) is a method used to model the linear relationship between a dependent variable and one or more independent variables MLR

is based on least squares: the model is fitted such that the sum-of-squares of differences of observed and predicted values is minimized

Let x1; x2; …; xr be a set of r predictors believed to be related to a response variable Y The linear regression model for the jth sample unit has the form:

Where ε is a random error and the βi, i=0, 1,…, r are unknown regression coefficients

In this paper, there are five independent variables and one dependent variable The relationships between these variables are of the following form:

Where:

F(x) - dependence variable

A (°C) - holding furnace temperature

B (°C) - die temperature

C (m/s) - plunger velocity 1st stage

D (m/s) - plunger velocity 2nd stage

E (bars) - multiplied pressure during the third phase

The results after analysing by Intercooled Stada 8.2 Software The final MVLR model equation for porosity after substituting regression coefficients is as follows:

F(x)= 3.054569 – 0.8844*10-3A – 0.83*10-3B - 0.03059C + 0.01754D – 0.00201E (3)

Fig 6 Experimental and predicted values of shrinkage porosity

Fig 6 show that: there is convincing agreement between experimental values and predicted values for Shrinkage porosity percent

3 RESULTS AND DISCUSSION

Matlab code for finding optimization shrinkage porosity value

Program in Matlab

clc;

clear all;

close all;

f = @(x)3.054569-0.8844e-3*x(1)-0.83e-3*x(2)-

0.03059*x(3)+0.01754*x(4)-0.00201*x(5);

options = optimset('GradObj','on');

[x,fval,exitflag,output] =

fmincon(f,[670;220;0.2;2.5;240],[],[],[],[],[600;180;0.05;1.5;200],[700;260;0.35;3.5;280],[],optimset('Dis play','iter'));

x

fval

Results: x =

700.0000 → A= 700°C 260.0000 B = 260°C 0.3500 C = 0.35 m/s 1.5000 D = 1.5 m/s 280.0000 E = 280 bar fval = 1.6725 Shrinkage porosity: 1.6725 %

By Program in Matlab we are known as the best combination in the 27 experimental configurations

This result is similar with ANOVA, the best combination for this study: A 3 B 3 C 3 D 1 E 3

CONCLUSIONS

In this paper, the optimum process parameters values predicted for casting of minimum shrinkage porosity (1.6725%), the best combination parameters given as follows:

Holding furnace temperature 700 °C

Plunger velocity, 1st stage 0.35 m/s Plunger velocity, 2nd stage 1.5 m/s Multiplied pressure 280 bar The model proposed in this paper gives satisfactory results for the optimization of pressure die casting process The predicted values of the process parameters and the calculated are in convincing agreement with the experimental values

REFERENCES

[1] M Avalle, G Belingardi, M.P Cavatorta, R Doglione, Casting defect and fatigue strength of a die cast aluminum alloy：a comparison between standard specimens and

production components International Journal of Fatigue 24, 2002, p 1∼9

[2] M Avalle, G Belingardi, M.P Cavatorta, Static and fatigue strength of a die cast

aluminum alloy under different feeding conditions Presented at EUROMAT 2001,

Rimini, p 10∼14

[3] Dargusch, M.S., Dour, G., Schauer, N., Dinnis, C.M & Savage, G, The influence of pressure during solidification of high pressure die cast aluminum telecommunications

components Journal of Materials Processing Technology, 2006, Vol 180, p 37∼43

[4] G.O Verran, Influence of injection parameters on defects formation in die casting

Al12Si1.3Cu alloy: Experimental results and numeric simulation Journal of Materials

Processing Technology, 2006, Vol 179, p 190∼195

[5] Mousavi Anijdan, S.H., Bahrami, A., Madaah Hoseini, H.R & Shafyei, A Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of

minimum porosity Materials and Design, 2006, Vol 27, p 605∼609

[6] Tsoukalas, V.D, A study of porosity formation in pressure die casting using the Taguchi

approach Journal of Engineering Manufacture, 2004, Vol 218, p 77∼86

[7] Tsoukalas, V.D Optimization of porosity formation in AlSi9Cu3 pressure die castings

using genetic algorithm analysis Materials and Design, 2008, Vol 29, p 2027∼2033 [8] Syrcos, G.P Die casting process optimization using Taguchi methods Journal of

Materials Processing Technology, 2003, Vol 135, p 68∼74

[9] ProCAST User Manual, ESI Group, Version 2010

AUTHOR’S INFORMATION

1 Anh Tuan Do Department of Mechanical Engineering, Hung Yen University of

Technology and Education, Dan Tien-Khoai Chau-Hung Yen, Vietnam

E-mail: airgun631@gmail.com Phone number: 0936631999

2 Tran Vung Vu Department of Mechanical Engineering, Hung Yen University of

Technology and Education, Dan Tien-Khoai Chau-Hung Yen E-mail:

vutranvung@gmail.com Phone number: 0982426620

3 Van Thuy Hoang Department of Mechanical Engineering, Vocational Intermediate

School No.14, Yen Son-Tam Diep-Ninh Binh, Vietnam Email:

hoangthuy030@gmail.com Phone number: 0976110086

4 Thi Mo Pham Department of Mechanical Engineering, Vocational Intermediate School

No.14, Yen Son-Tam Diep-Ninh Binh, Vietnam Email: sumosumo68@gmail.com

Phone number: 0918590388